Combining Knowledge Graph and Graph Algorithms to Find Hidden Skills at NASA

The author discusses the use of knowledge graphs at NASA's people analytics branch to identify hidden skills and competencies within employees. The author introduces the concept of a knowledge graph, which is a mechanism for connecting different data sources together via common relationships, allowing for the combination of structured and unstructured data. The author uses Neo4j, a graph database, and various algorithms such as node similarity, centrality, and community detection to build and analyze the knowledge graph. The author demonstrates how the knowledge graph can be used to identify skills gaps, career paths, succession planning, and strategic workforce alignment. The author also discusses the use of external data sources, such as the PDW (personal data warehouse) and mission data, to enrich the knowledge graph. Additionally, the author highlights the potential for graph data science to support diversity, equity, and inclusion initiatives, as well as ranking skills by occupation and employees similar to an occupation based on abilities. The author concludes that knowledge graphs have the potential to answer many questions within people analytics of human capital and looks forward to seeing what new technologies can be developed in this field.

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